Multitask Learning with Local Attention for Tibetan Speech Recognition
Joint Authors
Wang, Hui
Gao, Fei
Zhao, Yue
Yang, Li
Yue, Jianjian
Ma, Huilin
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-18
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In this paper, we propose to incorporate the local attention in WaveNet-CTC to improve the performance of Tibetan speech recognition in multitask learning.
With an increase in task number, such as simultaneous Tibetan speech content recognition, dialect identification, and speaker recognition, the accuracy rate of a single WaveNet-CTC decreases on speech recognition.
Inspired by the attention mechanism, we introduce the local attention to automatically tune the weights of feature frames in a window and pay different attention on context information for multitask learning.
The experimental results show that our method improves the accuracies of speech recognition for all Tibetan dialects in three-task learning, compared with the baseline model.
Furthermore, our method significantly improves the accuracy for low-resource dialect by 5.11% against the specific-dialect model.
American Psychological Association (APA)
Wang, Hui& Gao, Fei& Zhao, Yue& Yang, Li& Yue, Jianjian& Ma, Huilin. 2020. Multitask Learning with Local Attention for Tibetan Speech Recognition. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1145206
Modern Language Association (MLA)
Wang, Hui…[et al.]. Multitask Learning with Local Attention for Tibetan Speech Recognition. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1145206
American Medical Association (AMA)
Wang, Hui& Gao, Fei& Zhao, Yue& Yang, Li& Yue, Jianjian& Ma, Huilin. Multitask Learning with Local Attention for Tibetan Speech Recognition. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1145206
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1145206